Adult Image Classification by a Local-Context Aware Network

Xizi Wang, Feng Cheng, Shilin Wang, Huanrong Sun, Gongshen Liu, Cheng Zhou

Abstract: To build a healthy online environment, adult image recognition is a crucial and challenging task. Recent deep learning based methods have brought great advances to this task. However, the recognition accuracy and generalization ability need to be further improved. In this paper, a local-context aware network is proposed to improve the recognition accuracy and a corresponding curriculum learning strategy is proposed to guarantee a good generalization ability. The main idea is to integrate the global classification and the local sensitive region detection into one network and optimize them simulatenously. Such strategy helps the classification networks focus more on suspicious regions and thus provide better recognition performance. Two datasets containing over 150,000 images have been collected to evaluate the performance of the proposed approach. From the experiment results, it is observed that our approach can always achieve the best classification accuracy compared with several state-of-the-art approaches investigated.